******(1) USE K-MEANS CLUSTERING TO FIND NEAREAST NEIGHBORS****************;proc modeclus data = sashelp.iris m = 1 k = 4 out = _test1 neighbor; var petallength petalwidth sepallength sepalwidth; ods output neighbor = _test2;run;ods html style = harvest image_dpi = 400;proc sgplot data=_test1; scatter y = density x = species / datalabel = cluster;run;data _test3; set _test2; retain _tmpid; if missing(id) = 0 then _tmpid = id; else id = _tmpid;run;data _test4 _test5; set _test3; by id notsorted; if first.id then neighbor = 0; neighbor + 1; output _test4; if last.id then output _test5;run;ods graphics / width = 6in height = 1in ;proc sgplot data = _test4; vbar id / response = distance group = neighbor; xaxis display = none grid;run;proc sgplot data = _test5; series x = id y = neighbor; xaxis display = none grid; yaxis values = (1 to 6) label = 'No. of neighbors';run;******(2) PARTITION RAW DATASET INTO TRAINING AND VALIDATION DATASETS******;%macro partition(data = , target = , smpratio = , seed = , train = , validate = );/*************************************************************** MACRO: partition()* GOAL: divide to training and validation sets that* represent original target variable's proportion* PARAMETERS: data = raw dataset* target = target variable* smprate = ratio between training and validation* set * seed = random seed for sampling**************************************************************/ods select none;ods output variables = _varlist;proc contents data = &data;run;proc sql; select variable into: num_var separated by ' ' from _varlist where lowcase(type) = 'num';quit;proc sort data = &data out = _tmp1; by ⌖run;proc surveyselect data = _tmp1 samprate = &smpratio out = _tmp2 seed = &seed outall; strata &target / alloc = prop;run;data &train &validate; set _tmp2; keep &num_var ⌖ if selected = 0 then output &train; else output &validate;run;proc datasets nolist; delete _:;quit;ods select all;%mend;%partition(data = sashelp.iris, target = species, smpratio = 0.5, seed = 20110901, train = iris_train, validate = iris_validate);%partition(data = sashelp.cars, target = origin, smpratio = 0.5, seed = 20110901, train = cars_train, validate = cars_validate);********(3) BUILD A USER-DEFINED FUNCTION TO IMPLEMENT K-NN************************;option mstored sasmstore = sasuser;%macro knn_macro / store source;%let target = %sysfunc(dequote(&target));%let input = %sysfunc(dequote(&input));%let train = %sysfunc(dequote(&train));%let validate = %sysfunc(dequote(&validate));%let error = 0;%if %length(&k) = 0 %then %do;%put ERROR: Value for K is missing ;%let error = 1;%end;%else %if %eval(&k) le 0 or %sysfunc(anydigit(&k)) = 0 %then %do;%put ERROR: Value for K is invalid ;%let error = 1;%end;%if %length(&target) = 0 %then %do;%put ERROR: Value for target is missing ;%let error = 1;%end;%if %length(&input) = 0 %then %do;%put ERROR: Value for INPUT is missing ;%let error = 1;%end;%if %sysfunc(exist(&train)) = 0 %then %do; %put ERROR: Training dataset does not exist ;%let error = 1;%end;%if %sysfunc(exist(&validate)) = 0 %then %do;%put ERROR: validation dataset does not exist ;%let error = 1;%end;%if &error = 1 %then %goto finish;ods output classifiedtestclass = _classifiedtestclass;proc discrim data = &train test = &validate testout = _scored method = npar k = &k testlist ; class ⌖ var &input;run;data _null_; set _scored nobs = nobs end = eof; retain count; if &target ne _into_ then count + 1; if eof then do; misc = count / nobs; call symput('misc', misc); end;run;%finish:;%mend;proc fcmp outlib = sasuser.knn.funcs;/************************************************************ FUNCTION: knn() * GOAL: apply k-Nearest-Neighbor for classification* INPUT: k = number of nearest neighbours* train = training dataset* validate = validation dataset* target = target variable* input = input variables* OUTPUT: overall misclassification rate***********************************************************/ function knn(k, train $, validate $, target $, input $); rc = run_macro('knn_macro', k, train, validate, target, input, misc); if rc eq 0 then return(misc); else return(.); endsub;run;******(3) APPLY K-NN FUNCTION TO CLASSIFY IRIS AND CARS DATA****************;%macro errorchk(train = , validate = , target = , input = , k = );/************************************************************ MACRO: errorchk() * GOAL: use knn()function and visualize result* PARAMETERS: train = training dataset* validate = validation dataset* target = target variable* input = input variables* k = number of nearest neighbors***********************************************************/option cmplib = (sasuser.knn) mstored sasmstore = sasuser;data _null_; misc_rate = knn(&k, symget('train'), symget('validate'), symget('target'), symget('input')); call symput('misc_rate', misc_rate);run;proc sql noprint; select distinct &target into : varlist1 separated by ' ' from &validate; select distinct cats("'", lowcase(&target), "'") into: varlist2 separated by ',' from &validate;quit;proc transpose data = _classifiedtestclass out = _out1; by from&target notsorted; var &varlist1;run;data _out2; set _out1; where lowcase(from&target) in (&varlist2); label _name_ = 'Level';run;proc sgplot data = _out2; vbar from&target / response = col1 group = _name_; xaxis label = 'Real'; yaxis label = 'Classified '; inset "Overall misclassification rate is: %sysfunc(putn(&misc_rate, percent8.2))" / position = topright;run;%mend;ods graphics / width= 400px height = 300px ;%errorchk(train = iris_train, validate = iris_validate, target = species, input = petallength petalwidth sepallength sepalwidth, k = 5);%errorchk(train = cars_train, validate = cars_validate, target = origin, input = invoice wheelbase length, k = 5);******(4) VISUALIZE CLASSIFICATION RESULT FOR CARS DATA********************;proc rank data = _scored groups = 4 out = _out3; var invoice; ranks q;run;proc sort data = _out3 out = _out3; by q invoice;run;data _out4; set _out3; by q ; retain fmtname "qvar" start end; if first.q then start = invoice; if last.q then end = invoice; if last.q; length label $35; q + 1; label = cat(q,'Qu.:', '$',start,'-','$',end);run;proc format cntlin = _out4 fmtlib;run;data _out5(keep=level name invoice wheelbase length q qfmt); set _out3; qfmt = put(invoice, qvar.); level = _into_; name = '2-classified'; output; level = origin; name = '1-real'; output;run;ods graphics / width = 700px height = 500px ;proc sgpanel data = _out5; panelby qfmt name / layout = lattice onepanel novarname; scatter x = Wheelbase y = length / group = level ;run;******(5) USE LOGISTIC REGREESION TO CLASSIFY CARS DATA********************;proc logistic data = cars_train; model origin = invoice wheelbase length / link = glogit; score data = cars_validate out = logitscored;run;proc freq data = logitscored; table f_origin*i_origin / nocol nocum nopercent;run;******(6) RUN LOOPS ON K-NN TO FIND THE BEST K VALUE***********************;%macro findk(train = , validate =, target = , input =, maxk =);/************************************************************ MACRO: findk()* GOAL: visualize results of k-NNs by loops* PARAMETERS: train = training dataset* validate = validation dataset* target = target variable* input = input variables* maxk = maximum value of k***********************************************************/option cmplib = (sasuser.knn) mstored sasmstore = sasuser;ods select none;data _tmp3; do k = 1 to &maxk ; misc_rate = knn(k, symget('train'), symget('validate'), symget('target'), symget('input')); output; end;run;proc sql; select min(misc_rate) into: min_misc from _tmp3; select k into: bestk separated by ', ' from _tmp3 having misc_rate = min(misc_rate);quit;ods select all;proc sgplot data = _tmp3; series x = k y = misc_rate; xaxis grid values = (1 to &maxk by 1) label = 'k number of neareast neighbours'; yaxis grid values = ( 0 to 0.5 by 0.05) label = 'Misclassification rate'; refline &min_misc / transparency = 0.3 label = "k = &bestk"; format misc_rate percent8.1;run;proc datasets nolist;delete _:;quit;%mend;ods html style = htmlblue;%findk(train = iris_train, validate = iris_validate, target = species, input = petallength petalwidth sepallength sepalwidth, maxk = 20);%findk(train = cars_train, validate = cars_validate, target = origin, input = invoice wheelbase length, maxk = 40);****** END OF ALL CODING *************************************************;
